Infrastructure
Market Blade’s computational backbone consists of a large cluster of Chinese-manufactured GPUs optimized for large VRAM and tremendously fast inference of AI models. Unlike NVIDIA GPUs, which typically handle 50-100 tokens per second for each request, allowing up to 400 parallel requests in batch, these units achieve a throughput of 2000 tokens per second for each request, allowing up to 400 parallel requests in batch, enabling rapid analysis of large datasets. The cluster is configured in a distributed architecture, supporting up to 500 concurrent requests with minimal latency.
Key infrastructure components include:
Data Ingestion Layer: A fault-tolerant Kafka-based system for streaming X data.
Processing Layer: GPU-accelerated nodes running PyTorch, vLLM for request management, and TPU-based processing, supporting custom tensor operations for real-time NLP and sentiment scoring.
Storage Layer: A hybrid database combining time-series storage (InfluxDB) for historical data and a high-speed cache (Redis) for real-time results.
API Layer: RESTful endpoints delivering processed insights to the front-end interface.
Scalability is achieved through dynamic load balancing, with additional nodes spun up during peak usage. The system processes approximately 10^8–10^9 raw lines of unique text data daily, compressing it into a 3–5-gigabyte analytical footprint for efficient querying.
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